Insights

Part Three

Reflecting on 2025, it’s clear that this year set the stage for some of the fastest technological shifts of this decade. Generative AI matured from experimentation to real deployment, cyber threats became more aggressive and more automated, and organizations across every industry began rethinking how they use data, cloud, and automation to stay competitive. 2026 is shaping up to be a defining year for technology with shifts in AI, infrastructure, security, and automation that are too big for any company to ignore. 

As decision-makers, it’s essential to understand not just what’s new, but why it matters for your business. Below are five key trends that deserve your attention in 2026, and what you should consider doing now to stay ahead. 

Part 4 of 4:

As small and mid-sized businesses (SMBs) increasingly leverage data for AI initiatives, they face a critical challenge: how to utilize sensitive information while maintaining privacy, security, and compliance. Traditional approaches like redaction—simply removing or masking sensitive elements—often strip data of its utility for AI training and analysis.

Part 3 of 4:

As AI adoption accelerates among small and mid-sized businesses, the focus often centers on capabilities and benefits. However, equally important—yet frequently overlooked—are the significant risks associated with AI training data. From compliance violations to bias perpetuation, the data you use to train AI systems can introduce substantial business, legal, and reputational risks.

Part 1 of 4:

In today's rapidly evolving technological landscape, AI has moved from buzzword to business necessity. But for small and mid-sized businesses, the path to AI implementation isn't always clear. While enterprise organizations deploy dedicated data science teams and custom solutions, SMBs need more accessible approaches. This is where AI augmentation shines – offering targeted AI capabilities that enhance existing systems and workflows rather than replacing them wholesale.

Part 2 of 4:

The difference between AI success and failure often comes down to one crucial element: data quality. Even the most sophisticated AI systems will underperform or fail entirely when fed poor-quality data. For small and mid-sized businesses (SMBs) embarking on AI initiatives, effective data preparation isn't just a technical consideration—it's a strategic imperative.

Part 4 of 4:

Climate change is intensifying natural disasters, and IT outages are on the rise. Is your business prepared for floods, wildfires, or storms that disrupt entire regions? In this post, we explore forward-thinking strategies such as predictive analytics and Internet of Things (IoT) sensors to help SMBs stay ahead of emerging climate-driven risks.

Part 3 of 4:

Technology alone won’t save you when disaster hits. Even the most advanced IT systems can fail if employees don’t know their roles or communication lines crumble under stress. In this post, we explore how people, processes, and clear communication strategies elevate disaster recovery from a mere technology checklist to a robust, organization-wide discipline.

Part 2 of 4:

Once you recognize the hefty toll of ignoring disaster recovery, the next logical step is to build a resilient IT infrastructure. In this post, we explore strategies—from robust backups to cloud-based failover systems—that ensure your business can bounce back quickly when disaster strikes.

Part 1 of 4:

Natural disasters and disruptive incidents don’t discriminate based on company size. Yet many small and mid-sized businesses (SMBs) still don’t have a solid disaster recovery plan. In regulated industries like finance and healthcare, the stakes are even higher. In this post, we explore the real costs of ignoring disaster recovery—and why proactive planning can be the difference between resilience and ruin.

Part 5 of 5:

Artificial Intelligence (AI) has become an essential tool for executives looking to gain a competitive edge, streamline operations, and improve decision-making. For small-to-medium business (SMB) executives, Large Language Models (LLMs) offer capabilities that were once reserved for enterprise-level organizations. From automating workflows to generating insightful analyses, these tools can help you stay ahead in a fast-paced market.

This blog post compares the leading LLM models available today, highlighting their strengths, limitations, and practical use cases. We’ll focus on ChatGPT, CoPilot Agents, Claude, Gemini, and Grok, providing insights into how each model can be a powerful tool for SMB executives. Additionally, we’ll examine how certain LLMs leverage tools like web search, vision, and text-to-video generation, providing added value through their subscriptions.